Mobile robot monocular vision navigation based on road region and boundary estimation

Author

Chang, Chin-Kai ; Siagian, Christian ; Itti, Laurent

Author_Institution

Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA

fYear

2012

fDate

7-12 Oct. 2012

Firstpage

1043

Lastpage

1050

Abstract

We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks the most likely road appearance. On the other hand, the latter detects the vanishing point and road boundaries to estimate the shape of the road. Our algorithm operates in urban road settings and requires no training or camera calibration to maximize its adaptability to many environments. We tested our system in 1 indoor and 3 outdoor urban environments using our ground-based robot, Beobot 2.0, for real-time autonomous visual navigation. In 20 trial runs the robot was able to travel autonomously for 98.19% of the total route length of 316.60m.

Keywords

image segmentation; mobile robots; navigation; object detection; robot vision; sampling methods; Beobot 2.0; GRVS method; Gabor response variance score method; boundary estimation; camera calibration; ground-based robot; mobile robot monocular vision navigation system; nonuniform sampling mechanism; real-time autonomous visual navigation; region segmentation; road appearance; road boundary detection; road region; urban road settings; vanishing point; Estimation; Image color analysis; Image segmentation; Navigation; Roads; Robots; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on

Conference_Location

Vilamoura

ISSN

2153-0858

Print_ISBN

978-1-4673-1737-5

Type

conf

DOI

10.1109/IROS.2012.6385703

Filename

6385703